

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
class        A__(a_							):
						"""simple docstring"""






						_A    :				torch.FloatTensor
						_A    :				torch.FloatTensor
						_A    :				Optional[torch.FloatTensor]       					=			None







class        A__(a_,  a_							):
						"""simple docstring"""






						_A    :				List[Any]       					=			2





						@register_to_config
						def __init__(    self  ,      _lowercase = 0.0_2  ,      _lowercase = 100  ,      _lowercase = 1.0_0_7  ,      _lowercase = 80  ,      _lowercase = 0.0_5  ,      _lowercase = 50  ,      )     -> Dict:
									# standard deviation of the initial noise distribution
									a_						:						Tuple 							= sigma_max

									# setable values
									a_						:						int 							= None
									a_						:						np.IntTensor 							= None
									a_						:						torch.FloatTensor 							= None  # sigma(t_i)





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase = None    )     -> torch.FloatTensor:
									return sample





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase = None    )     -> Optional[int]:
									a_						:						str 							= num_inference_steps
									a_						:						List[str] 							= np.arange(0  ,      self.num_inference_steps    )[::-1].copy()
									a_						:						List[str] 							= torch.from_numpy(_lowercase    ).to(_lowercase    )
									a_						:						int 							= [
									    (
									        self.config.sigma_max**2
									        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
									    )
									    for i in self.timesteps
									]
									a_						:						Optional[int] 							= torch.tensor(_lowercase  ,      dtype=torch.floataa  ,      device=_lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase = None    )     -> Tuple[torch.FloatTensor, float]:
									if self.config.s_min <= sigma <= self.config.s_max:
												a_						:						int 							= min(self.config.s_churn / self.num_inference_steps  ,      2**0.5 - 1    )
									else:
												a_						:						str 							= 0

									# sample eps ~ N(0, S_noise^2 * I)
									a_						:						Union[str, Any] 							= self.config.s_noise * randn_tensor(sample.shape  ,      generator=_lowercase    ).to(sample.device    )
									a_						:						Optional[Any] 							= sigma + gamma * sigma
									a_						:						Dict 							= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

									return sample_hat, sigma_hat





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase = True  ,      )     -> Union[KarrasVeOutput, Tuple]:
									a_						:						str 							= sample_hat + sigma_hat * model_output
									a_						:						Optional[Any] 							= (sample_hat - pred_original_sample) / sigma_hat
									a_						:						Optional[Any] 							= sample_hat + (sigma_prev - sigma_hat) * derivative

									if not return_dict:
												return (sample_prev, derivative)

									return KarrasVeOutput(
									    prev_sample=_lowercase  ,      derivative=_lowercase  ,      pred_original_sample=_lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase = True  ,      )     -> Union[KarrasVeOutput, Tuple]:
									a_						:						int 							= sample_prev + sigma_prev * model_output
									a_						:						Optional[int] 							= (sample_prev - pred_original_sample) / sigma_prev
									a_						:						Optional[int] 							= sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

									if not return_dict:
												return (sample_prev, derivative)

									return KarrasVeOutput(
									    prev_sample=_lowercase  ,      derivative=_lowercase  ,      pred_original_sample=_lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase    )     -> Optional[Any]:
									raise NotImplementedError()





from __future__ import annotations


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						List[str] 							= 0
			a_						:						str 							= len(a__) - 1

			while i < j:
						if nums[i] + nums[j] == target:
									return [i, j]
						elif nums[i] + nums[j] < target:
									a_						:						List[Any] 							= i + 1
						else:
									a_						:						Optional[Any] 							= j - 1

			return []


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(F"""{two_pointer([2, 7, 11, 15], 9) = }""")


